Among many different functions, the basic function of elevator group control is call allocation. The aim of allocation is to distribute the calls to be served by the elevator cars in such a way that one of the indicators describing the elevator system is optimized. Traditionally, the most commonly used indicators relate to call times and passenger waiting times. Typically, the average values of these are calculated and their distributions are determined. ‘Calls’ refers generally to all calls issued, i.e. both calls given by pressing up/down buttons at landings and destination floor calls given from an elevator car. The former are landing calls and the latter car calls. In addition, the calls include the calls needed in the so-called destination control method, which are issued via call input devices. In the destination control method, the elevator customer indicates his/her destination floor to the system already in the elevator lobby via a call input device, and in this case no separate call needs to be given in the elevator car.
There are various call allocation methods, and each elevator manufacturer have their own methods for implementing this function. However, a feature common to these different methods is that they employ a number of parameters characteristic to each method, by means of which the operation of the method is controlled. The control can be so implemented that in different traffic situations a set of parameters suited for the particular situation is applied. This is designed to give the system a possibility to adapt to the prevailing traffic situation, e.g. a peak traffic situation in the building.
In a prior-art control method, a traffic detector monitors the operation and state of the elevator system and determines the prevailing traffic type. The things to be monitored typically include the calls, the loads of the elevators and the motional states of the elevators. Depending on the traffic type detected, a parameter set tailored for this traffic type is introduced. For example, a set of parameters for outgoing peak traffic may give a greater weight to calls with a lobby floor as a destination than to calls issued from lobby floors. During a peak traffic condition, the objective may be to give a greater weight to minimizing the traveling time of the passengers in the car. When the aim is to minimize two or more aspects at the same time, this is a case of multi-objective optimization.
A difficulty in the above-described prior-art method is how to define the practical values of each parameter set in the parameter bank corresponding to different traffic situations. These parameters are sensitive to things like building type, number of floors, distribution of passengers between different floors of the building, number of elevators in the group, and properties of the elevators. In addition, actual traffic in the building varies, the number of elevator users at different floors in the building does not remain constant in the long run, and inaccuracies, detection errors and detection delays may easily occur in the operation of the traffic detector.
In practice, the parameter sets stored in the parameter bank have to be assigned compromise values such that will work reasonably well in a majority of deliveries without a need to change the parameters later on site. These parameter values may have been set e.g. on the basis of simulation operation or expert experience. It is obvious that this kind of average parameter values do not lead to the best possible operation the building, elevator group and traffic situation concerned in each case.
Another problem in selecting the parameter set on the basis of traffic type is how to choose the aspects to be weighted and how to determine the weighting values. It is possible to find numerous aspects to be optimized, such as call time, estimated passenger waiting time, riding time and traveling time, number of stops, car load, number of simultaneous car an landing calls, etc. The problem to be solved is which one of these aspects should be weighted and to what degree in each traffic situation. If the aspects are selected and the weighting values are fixed beforehand, then this is a choice made in advance by the designer, which does not necessarily correspond to the needs of the owner of the building. On the other hand, if the aspects to be optimized are not fixed beforehand, the operating personnel of the building can be considered as being allowed a freedom to decide for themselves about the weighting in different traffic situations. However, due to the complex nature of the adjustments and the matter as a whole, this is not a reasonable alternative.
Specification WO 02/066356 discloses a method for controlling an elevator group wherein the energy consumed by the elevator system is minimized so that a desired service time of elevator passengers if fulfilled on an average. In this method, a given service time of the elevator group is assigned a target value for call allocation. The service time used may be e.g. call time, passenger waiting time, traveling time or riding time.
In other words, the control method optimizes two incommensurable quantities of different types, i.e. waiting time and energy consumption. In the method according to specification WO 02/066356, to render these quantities commensurable and mutually comparable, the routes R of the elevators are selected in such a way that the cost termC=WTTN(R)+WEEN(R)  (1)is minimized. TN(R) is a normalized sum of call times for route alternative R and correspondingly EN(R) is normalized energy consumption for route alternative R. WT and WE are the weighting coefficients of the above-mentioned cost terms, such that0≦WT≦1 and WE=1−WT.  (2)
Individual waiting times are exponentially distributed, but their sum T(R) roughly follows a normal distribution, so they allow the application of normalization TN(R)=(T(R)−μT)/σT. Similarly for the energy term EN(R)=(E(R)−μE)/σE. The expected values μ and mean distributions σ are the indicators for the whole set of aspects, i.e. for the route alternatives for the elevators suited to the current situation. In practice, since the number of route alternatives increases exponentially with the number of calls, sample quantities are resorted to: instead of the expected value, sample mean values T and Ē are used, and instead of the mean distribution, sample mean distributions ST and SE are used. As a result,TN(R)≈(T(R)− T(R′))/ST(R′) andEN(R)≈(E(R)− E(R′))/SE(R′),  (3)where R′ is a number of randomly generated route alternatives sufficient to produce reliable estimates for μ and σ. After the normalization, both optimization targets approximately follow the distribution N(0,1) and can thus be summed without problems.
When calls are allocated in this manner, we can distinguish in the operation of the system two different extreme situations, i.e. WT=1 and WE=0, and on the other hand WT=0 and WE=1. In the first situation, the optimization system finds such routes for the elevators that the total waiting time for the calls is as short as possible regardless of energy consumption. In the second situation, the optimization system arranges the routes in such a way that the elevators will consume as little energy as possible but the total waiting time is neglected. It can be considered that optimization of waiting times and optimization of energy consumption are contrary objectives, because when only one of the objectives is optimized, the other objective suffers. Between the aforesaid extreme situations, the operating point can be moved in a sliding manner by selecting the weighting coefficients WT and WE in accordance with equation (2).
Although there are now only two aspects to be optimized and by changing the weightings of these it is possible to move steplessly from pure waiting time optimization to pure energy consumption optimization, there remains the difficult question of how to define the weighting coefficients WT and WE. The weighting coefficients should be set on a suitable basis so that they are applicable to different identified traffic types and intensities in the location of the elevator system concerned. In the prior-art method, the basic aim was to allocate the calls in such a way that a given passenger service time, such as e.g. waiting time, remains at a certain average level regardless of the traffic situation and intensity. By selecting suitable parameters WT and WE for each traffic situation, the set objective regarding waiting time is attained. At the same time, the amount of energy needed for transporting the passengers can be reduced because no unnecessary effort is made to provide faster service to customers than required by the set waiting time objective.
In the prior-art method, traffic identification and parameter sets bound with it are effectively eliminated by methods known from control system engineering. In control system engineering, it is an objective to control a process in such a way that that the quantity to be controlled remains at its target value as well as possible. The idea is to compare the controlled quantity to a set value and to generate from the error between these a control quantity that can be used to direct the operation of the system in the correct direction so that the error between the set value and the controlled quantity disappears.
It is desirable that the average waiting time of passengers traveling in the elevator system can be controlled. In prior art, real-time measurements of this quantity are obtained via landing call buttons. A call is activated when a passenger entering the system issues the call, and it is removed when the elevator to which it was allocated starts decelerating to the floor and simultaneously resets the call. Individual call times thus realized are compared to a set target time.
As the results of these individual call time measurements vary over a wide time range from zero to values as high as over 90 seconds, the prior-art method uses only the integrating block of the three possible blocks of a PID controller. The integrating block drives the mean error to zero. In the control method, a sufficiently long integrating time constant has to be selected to ensure that an individual measurement significantly differing from the mean value will not have an excessive effect on the control, but the time constant has to be short enough to allow the system to react to changes occurring in the traffic type and intensity.
From the output of the integrating controller, the weighting coefficient WE for the optimization of energy consumption is obtained directly. From this is further obtained the weighting coefficient WT for the optimization of waiting time according to equation (2). It is thinkable that, in a situation where the actual measured call time is the same as the target value of call time, call times have been perfectly optimized without regard to energy consumption. In this case, the zero value at the controller output is also the weighting coefficient used for energy consumption. If, for example, the average of actual call times shifts to a level lower than the target, in other words, if the system is serving too well as compared to the target set in optimization e.g. due to a quieter traffic situation, then the error will become greater. As a consequence, weighting coefficient WE increases and WT decreases, so the waiting times indicated by the waiting time characteristic become longer; in other words, the significance of energy consumption in the selection of route alternatives increases and the significance of call times decreases.
The actual allocation of elevators to the calls issued, i.e. the calculations for finding the most optimal route alternative for the elevators of the elevator system are performed by an optimizer. The optimizer receives as input data the weighting coefficients calculated by the controller. In addition, the optimizer is supplied with information regarding the position of each elevator in the elevator system, whether the elevator is currently transporting passengers, going to serve a landing call or whether it is in a rest position. Based on the motional state and position of the elevators and the existing calls, the optimizer calculates the value of a cost function for the possible route alternatives for the elevators and provides an output giving the control system information regarding elevator routing that will minimize the cost function. The model of an elevator in the elevator system must obey the same rules of behavior as the actual elevator.
By the prior-art method for controlling an elevator group, when the target time is 20 seconds, it is possible to are an energy saving of 30-40% as compared to pure waiting time optimization with 0 seconds as the target time. As the waiting time target in the prior-art method can be easily understood and perceived, it is possible to make a simple user interface for it and the setting of the target waiting time can even be entrusted to the personnel of the building. It is also possible to create a programmable calendar of waiting times, allowing different service time targets to be set for different days of the week and times of the day.
The operation of the prior-art method for controlling an elevator group can be improved. The problem arises from the fact that the quantity measured in the prior-art method is actual call times. These measured actual call times are very variable, in other words, the standard deviation of call times is relatively large. From this it follows that the optimizer is unable to function in the best possible manner. If it were possible to predict with a sufficient accuracy the elevator call times to appear in the near future, then the calculation delays of the prior-art method could be reduced and therefore the optimizer could perform the calculations more efficiently. If additionally the standard deviation of the call times included in the prediction could be reduced, then the performance of the optimizer could be improved. These improvements can be implemented by applying the present invention.